Implement basic automation with Python.
You can manipulate data in the CDB with the help of XML files or the UI, however, these approaches are not well suited for programmatic access. NSO includes libraries for multiple programming languages, providing a simpler way for scripts and programs to interact with it. The Python Application Programming Interface (API) is likely the easiest to use.
This section will show you how to read and write data using the Python programming language. With this approach, you will learn how to do basic network automation in just a few lines of code.
The environment setup that happens during the sourcing of the ncsrc file also configures the PYTHONPATH environment variable. It allows the Python interpreter to find the NSO modules, which are packaged with the product. This approach also works with Python virtual environments and does not require installing any packages.
Since the ncsrc file takes care of setting everything up, you can directly start the Python interactive shell and import the main ncs module. This module is a wrapper around a low-level C _ncs module that you may also need to reference occasionally. Documentation for both of the modules is available through the built-in help() function or separately in the HTML format.
If the import ncs statement fails, please verify that you are using a supported Python version and that you have sourced the ncsrc beforehand.
Generally, you can run the code from the Python interactive shell but we recommend against it. The code uses nested blocks, which are hard to edit and input interactively. Instead, we recommend you save the code to a file, such as script.py, which you can then easily run and rerun with the python3 script.py command. If you would still like to interactively inspect or alter the values during the execution, you can use the import pdb; pdb.set_trace() statements at the location of interest.
With NSO, data reads and writes normally happen inside a transaction. Transactions ensure consistency and avoid race conditions, where simultaneous access by multiple clients could result in data corruption, such as reading half-written data. To avoid this issue, NSO requires you to first start a transaction with a call to ncs.maapi.single_read_trans() or ncs.maapi.single_write_trans(), depending on whether you want to only read data or read and write data. Both of them require you to provide the following two parameters:
user: The username (string) of the user you wish to connect as
context: Method of access (string), allowing NSO to distinguish between CLI, web UI, and other types of access, such as Python scripts
These parameters specify security-related information that is used for auditing, access authorization, and so on. Please refer to AAA infrastructure for more details.
As transactions use up resources, it is important to clean up after you are done using them. Using a Python with code block will ensure that cleanup is automatically performed after a transaction goes out of scope. For example:
In this case, the variable t stores the reference to a newly started transaction. Before you can actually access the data, you also need a reference to the root element in the data tree for this transaction. That is, the top element, under which all of the data is located. The ncs.maagic.get_root() function, with transaction t as a parameter, achieves this goal.
Once you have the reference to the root element, say in a variable named root, navigating the data model becomes straightforward. Accessing a property on root selects a child data node with the same name as the property. For example, root.nacm gives you access to the nacm container, used to define fine-grained access control. Since nacm is itself a container node, you can select one of its children using the same approach. So, the code root.nacm.enable_nacm refers to another node inside nacm, called enable-nacm. This node is a leaf, holding a value, which you can print out with the Python print() function. Doing so is conceptually the same as using the show running-config nacm enable-nacm command in the CLI.
There is a small difference, however. Notice that in the CLI the enable-nacm is hyphenated, as this is the actual node name in YANG. But names must not include the hyphen (minus) sign in Python, so the Python code uses an underscore instead.
The following is the full source code that prints the value:
As you can see in this example, it is necessary to import only the ncs module, which automatically imports all the submodules. Depending on your NSO instance, you might also notice that the value printed is True, without any quotation marks. As a convenience, the value gets automatically converted to the best-matching Python type, which in this case is a boolean value (True or False).
Moreover, if you start a read/write transaction instead of a read-only one, you can also assign a new value to the leaf. Of course, the same validation rules apply as using the CLI and you need to explicitly commit the transaction if you want the changes to persist. A call to the apply() method on the transaction object t performs this function. Here is an example:
You can access a YANG list node like how you access a leaf. However, working with a list more resembles working with Python dict than a list, even though the name would suggest otherwise. The distinguishing feature is that YANG lists have keys that uniquely identify each list item. So, lists are more naturally represented as a kind of dictionary in Python.
Let's say there is a list of customers defined in NSO, with a YANG schema such as:
To simplify the code, you might want to assign the value of root.customers.customer to a new variable our_customers. Then you can easily access individual customers (list items) by their id. For example, our_customers['ACME'] would select the customer with id equal to ACME. You can check for the existence of an item in a list using the Python in operator, for example, 'ACME' in our_customers. Having selected a specific customer using the square bracket syntax, you can then access the other nodes of this item.
Compared to dictionaries, making changes to YANG lists is quite a bit different. You cannot just add arbitrary items because they must obey the YANG schema rules. Instead, you call the create() method on the list object and provide the value for the key. This method creates and returns a new item in the list if it doesn't exist yet. Otherwise, the method returns the existing item. And for item removal, use the Python built-in del function with the list object and specify the item to delete. For example, del our_customers['ACME'] deletes the ACME customer entry.
In some situations, you might want to enumerate all of the list items. Here, the list object can be used with the Python for syntax, which iterates through each list item in turn. Note that this differs from standard Python dictionaries, which iterate through the keys. The following example demonstrates this behavior.
Now let's see how you can use this knowledge for network automation.
No previous NSO or netsim processes are running. Use the ncs --stop and ncs-netsim stop commands to stop them if necessary.
Leveraging one of the examples included with the NSO installation allows you to quickly gain access to an NSO instance with a few devices already onboarded. The getting-started/developing-with-ncs set of examples contains three simulated routers that you can configure.
Navigate to the 0-router-network directory with the following command.
You can prepare and start the routers by running the make and netsim commands from this directory.
With the routers running, you should also start the NSO instance that will allow you to manage them.
In case the ncs command reports an error about an address already in use, you have another NSO instance already running that you must stop first (ncs --stop).
Before you can use Python to configure the router, you need to know what to configure. The simplest way to find out how to configure the DNS on this type of router is by using the NSO CLI.
In the CLI, you can verify that the NSO is managing three routers and check their names with the following command:
To make sure that the NSO configuration matches the one deployed on routers, also perform a sync-from action.
Let's say you would like to configure the DNS server 192.0.2.1 on the ex1 router. To do this by hand, first enter the configuration mode.
Then navigate to the NSO copy of the ex1 configuration, which resides under the devices device ex1 config path, and use the ? and TAB keys to explore the available configuration options. You are looking for the DNS configuration.
...
Once you have found it, you see the full DNS server configuration path: devices device ex1 config sys dns server.
As an alternative to using the CLI approach to find this path, you can also consult the data model of the router in the packages/router/src/yang/ directory.
As you won't be configuring ex1 manually at this point, exit the configuration mode.
Instead, you will create a Python script to do it, so exit the CLI as well.
You will place the script into the ex1-dns.py file.
In a text editor, create a new file and add the following text at the start.\
The root variable allows you to access configuration in the NSO, much like entering the configuration mode on the CLI does.
Next, you will need to navigate to the ex1 router. It makes sense to assign it to the ex1_device variable, which makes it more obvious what it refers to and easier to access in the script.
In NSO, each managed device, such as the ex1 router, is an entry inside the device list. The list itself is located in the devices container, which is a common practice for lists. The list entry for ex1 includes another container, configwhere the copy of ex1 configuration is kept. Assign it to the ex1_config variable.
Alternatively, you can assign to ex1_config directly, without referring to ex1_device, like so:
This is the equivalent of using devices device ex1 config on the CLI.
For the last part, keep in mind the full configuration path you found earlier. You have to keep navigating to reach the server list node. You can do this through the sys and dns nodes on the ex1_config variable.
DNS configuration typically allows specifying multiple servers for redundancy and is therefore modeled as a list. You add a new DNS server with the create() method on the list object.
Having made the changes, do not forget to commit them with a call to apply() or they will be lost.
Alternatively, you can use the dry-run parameter with the apply_params() to, for example, preview what will be sent to the device.
Lastly, add a simple print statement to notify you when the script is completed.
Save the script file as ex1-dns.py and run it with the python3 command.
You should see Done! printed out. Then start the NSO CLI to verify the configuration change.
Finally, you can check the configured DNS servers on ex1 by using the show running-config command.
If you see the 192.0.2.1 address in the output, you have successfully configured this device using Python!
The code in this chapter is intentionally kept simple to demonstrate the core concepts and lacks robustness in error handling. In particular, it is missing the retry mechanism in case of concurrency conflicts as described in Handling Conflicts.
Perhaps you've wondered about the unusual name of Python ncs.maagic module? It is not a typo but a portmanteau of the words Management Agent API (MAAPI) and magic. The latter is used in the context of so-called magic methods in Python. The purpose of magic methods is to allow custom code to play nicely with the Python language. An example you might have come across in the past is the __init__() method in a class, which gets called whenever you create a new object. This one and similar methods are called magic because they are invoked automatically and behind the scenes (implicitly).
The NSO Python API makes extensive use of such magic methods in the ncs.maagic module. Magic methods help this module translate an object-based, user-friendly programming interface into low-level function calls. In turn, the high-level approach to navigating the data hierarchy with ncs.maagic objects is called the Python Maagic API.